Meta-analysisMeta-analyses typically combine two or more independent trials (often with different designs and conducted for different reasons) into the same analytic framework. In a conceptually similar study, statistical methods are used for meta-analysis to arrive at common truths. In addition to providing estimates of unknown general truths, a meta-analysis is able to compare the results of different studies and determine the patterns between the results, the sources of divergence between these results, or other interesting relationships that may be exposed in multiple researches. While a meta-analysis has long been part of the experiment, its complexity is problematic in terms of planning and implementation.

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Our experts will help you to develop a clear and comprehensible plan to process meta-analyses. We will provide this service through the steps as follows (Figure 1):

The steps of meta-analysis

Figure 1. The steps of a meta-analysis

Due to the complexity of meta-analyses, we are mainly dedicated to solving the following problems:

  • Confirming the studies that should be combined in the meta-analysis

There are many studies associated with trials, so the first challenge is to identify the studies that should be combined in the meta-analysis. One way to create an analysis pool is to include only very similar studies to eliminate heterogeneity to help reduce variability in the outcome assessment. On the other hand, design and population heterogeneity can be accepted through broader inclusion of research, which can then be analyzed to inform interested clinical issues. We will try our best to select the appropriate studies to help you design relevant trials.

  • Dealing with heterogeneity in the meta-analysis

Heterogeneity refers to differences among studies and/or study results. When dealing with the clinical heterogeneity of a meta-analysis, if the focus is on effectiveness in a single setting, additional calculations (such as prediction intervals) should be considered. If there is no clear answer to the question raised, strategies to explain heterogeneity (such as subgroup analysis, meta-regression) can be adopted. We will analyze the whole process and data of the trials to help you deal with heterogeneity in the meta-analysis.

  • Choosing proper statistical methods for the meta-analysis

There are lots of statistical techniques used to combine individual study results currently. The simplest technique is based on a fixed-effect model, which assumes that the true effects of all studies are the same. We will select the appropriate statistical methods according to the experimental features to help you complete the meta-analysis.

  • Using IPD to summary data

Both individual-level and aggregate-level approaches should be considered for meta-analyses. To distinguish between the two approaches, we refer to meta-analyses based on individual patient data as 'IPD meta-analyses', and to those based on aggregate patient data as 'APD meta-analyses'. Anything you can do in an APD meta-analysis can also be completed with an IPD meta-analysis, but the converse is not true. What's more, access to patient-level data provides greater flexibility and is desirable compared with using only summary-level information in most clinical trials. We will provide you with more effective IPD meta-analyses to summary data.

We guarantee the confidentiality and sensitivity of our customers' data. We are committed to providing you with timely and high-quality deliverables. At the same time, we guarantee cost-effective, complete and concise reports.

If you are unable to find the specific service you are looking for, please feel free to contact us.

1. Dersimonian R, Laird N. (1986) ‘Meta-analysis in clinical trials’, Controlled Clinical Trials, 7(3):177. 2. Kriston L. (2013) ‘Dealing with clinical heterogeneity in meta-analysis. Assumptions, methods, interpretation’, International Journal of Methods in Psychiatric Research, 22(1):1-15. 3. Brockwell S E, Gordon I R. (2001) ‘A comparison of statistical methods for meta-analysis’, Statistics in Medicine, 20(6):825–840. 4. Berlin J A, Crowe B J, Whalen E, et al. (2013) ‘Meta-analysis of clinical trial safety data in a drug development program: Answers to frequently asked questions’, Clinical Trials, 10(1):20-31.

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